Automatic semantic modeling of indoor scenes from low-quality RGB-D data using contextual information
ACM Trans. Graph., 2014.
We present a novel solution to automatic semantic modeling of indoor scenes from a sparse set of low-quality RGB-D images. Such data presents challenges due to noise, low resolution, occlusion and missing depth information. We exploit the knowledge in a scene database containing 100s of indoor scenes with over 10,000 manually segmented an...More
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